In the field of medical imaging, medical images are often required to be aligned for comparison. For example, a current image of a patient may be aligned with a prior image of the same patient to assess disease progression or results of treatment, for example to compare tumor size.
It is known to compare or combine images that have been obtained by different modalities of imaging, for example X-ray computed tomography (CT) and magnetic resonance imaging (MR), to take advantage of the different information that is obtained from scans in each modality.
However, in general, the position of anatomical features will differ between different images, for example due to different patient positioning, patient movement, different modalities of imaging or different imaging parameters. In order to achieve accurate alignment of two images, it is necessary to transform one of the images so that the coordinates of each anatomical feature are the same in each resulting image. This is achieved by the process of image registration and transformation.
Rigid registration refers to a class of techniques for aligning two or more images or volumes by way of rigid transformations (transformations that involve only rotation and translation parameters). A useful application of rigid registration is for alignment of current and prior volumes in a follow-up study. Affine registration is a registration using affine transformations (rotation, translation, scaling, or shearing).
For image registration, it is sometimes useful to use rotation, translation, and a uniform scaling component. This gives a space of transformations that is more general than rigid transformations, but not as general as affine transformations.
Non-rigid registration refers to a class of techniques that use more general transformations that allow for deformation, including local deformation. These may be particularly suitable when registering images of organs or soft tissue.
Techniques for registration of images are well-known. In general, registration is an optimization problem, with the aim of finding an optimal transform between two images, which relates corresponding features in the images by mapping points in the coordinate system of one image onto the corresponding points in the coordinate system of the other image.
A similarity measure is a measure of the similarity between two images. For example, in the mutual information (MI) approach, points in each image are identified and correlated by the statistical similarity (mutual information) between the two images.
For registration of two images, one image may be kept constant and the other is transformed according to a set of parameters defined by the type of registration (for example, in rigid registration, rotation and translation parameters in the appropriate number of dimensions). The similarity measure between the two resulting images is then determined. This defines an objective function from the parameters to the similarity measure. The objective function is then optimized using an optimization function, for example, gradient descent, hill climbing or Powell optimization, to achieve an optimal transform relating the two images.
This optimal transform is applied to the second image to obtain a transformed image that is aligned with the first image, and has a common coordinate system.
Registration can be performed manually (for example, by manual selection of corresponding points on each image), semi-automatically, or automatically. Many medical imaging systems now have greater automation of registration than was previously the case.
Registration is not always successful, or even possible. Two cases of registration failure in an attempted registration of two sets of image data are described below.
In a first failure case, a good registration of the two sets of image data exists, but the registration algorithm fails to find it. In this case, it would be desirable to improve the registration algorithm to improve the chances of success.
In a second failure case, there is no possible good registration of the two sets of image data, because there is no overlap between the input data sets. For example, the two data sets that the registration algorithm is attempting to register represent images of completely different parts of the body, such as a head and a foot. In this case, no algorithm could possibly give a good registration. If an algorithm claims to find a registration for an unregistrable pair of images, this may be referred to as a blunder.
When registration of a pair of images has failed, a user may want to use an alternative method of alignment, or may want to identify that the pair of images for registration were incorrectly selected. Therefore, it is necessary for a system that is implementing a registration algorithm to be able to detect failures. Currently, most registration algorithms assume that a registration is possible, and always accept the result of the registration. This means that failed registrations, including blunders, are not detected automatically.
Registration algorithms may be evaluated by comparison to ground truth, for example, by comparison to accurate information such as that provided by a clinical expert. It is also known to evaluate registration algorithms without ground truth by considering all combinations of registrations between a set of volumes using multiple different algorithms and statistically combining the results. For this process to be most effective, many data sets and many registration algorithms are used, with considerable statistical processing. This process is used for off-line evaluation of registration algorithms.